We absolutely need the mechanics and the connections, but we also need the big picture (meta-analysis) - they are complementary
Synthesis is the combination of two or more entitites - studies, regions, etc., the integration of evidence. Maps are useful - where the work has been done.
Zen - mind and body, zen synthesis - mind is synthesis, body is data.
Systematic review are similar to meta-analysis. A meta-analysis always has statistics, not always a p-value but gets effect sizes, variance, etc. The systematic review is one-step below meta-analysis.
The goal is to identify an effect size or average effort that matters to the big picture.
Synthesis is a counterweight to hyper-specialization - reduces silo effect among disciplines and sub-disciplines. Diversity promotes discovery, conceptualizes complexity, and copes with data deluge.
Synthesis will shift your view of statistical significance.
Effect sizes much more important than p-values, the strength of the difference - important for primary studies.
Synthesis framing - from low to hi - natural concepts -> functional concepts -> integrative concepts.
Steps of meta-analysis - questions, set of studies, predictions (summarize knowledge, meta-analysis). Each study is a sample point.
Systematic review has ‘pre-specified eligibility criteria’ aka your search terms for papers in the primary literature. This commonly uses a PRISMA report or approach, e.g., we started with 2000 papers, filtered down to 20. Can use Web of Science, SCOPUS, Google Scholar - use all. There are R packages to do this!!
Meta-analysis/systematic review also very important for gap analysis.
Meta-analysis always has an effect size, what was the strength of evidence plus variance and a measure of difference.
Effect size measured as:
Full workflow here
Step 1 notes: Search multiple search terms, don’t forget to remove duplicates.
In Web of Science - View Citation Report top right for summary of search results!!!! Export as text or csv.
The idea is to saves results from multiple searches (as text file), then cross-references for duplicates, relevance (by title/abstract), make final list (sort)
step 2 PRISMA - preferred reporting items for systematic review and meta-analysis
library(tidyverse)
library(PRISMAstatement)
prisma(found = 156,
found_other = 0,
no_dupes = 53,
screened = 53,
screen_exclusions = 0,
full_text = 53,
full_text_exclusions = 24,
qualitative = 0,
quantitative = 29,
width = 800, height = 800)
You can use an evidence map to summarize. This is also where a map (or heatmap) or stacked barplot can go. Summarizes how well we have explored these topics.
All stats are a tool and a means to an end, not the end. In most cases, a simple test is appropriate (t-test, ANOVA, Chi-squared test) where each study is an independent sample.
Effect size plots can be viewed with a forest plot.
Reference: Meta-Analysis with R, look for e-book though.